# 导入需要使用的库 import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix import seaborn as sns import matplotlib.pyplot as plt # 读取Excel表格 data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') # 定义自变量和因变量 X = data.iloc[:, :-1].values y = data.iloc[:, -1].values # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # 定义随机森林分类器 classifier = RandomForestClassifier(n_estimators=100, random_state=0) # 在训练集上训练分类器 classifier.fit(X_train, y_train) # 在测试集上进行预测 y_pred = classifier.predict(X_test) # 计算预测精度并输出 accuracy = classifier.score(X_test, y_test) print("Accuracy:", accuracy) # 计算混淆矩阵并绘制热力图 cm = confusion_matrix(y_test, y_pred) sns.heatmap(cm, annot=True, cmap="Blues") plt.show()热力图改成小数点
时间: 2024-02-17 16:59:41 浏览: 68
要将热力图中的数字改为小数点,可以使用seaborn库中的annot_kws参数,指定热力图的字体大小、颜色等属性。具体实现代码如下:
```python
# 计算混淆矩阵并绘制热力图
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm/np.sum(cm), annot=True, fmt='.2%', cmap="Blues", annot_kws={"size": 14})
plt.show()
```
其中,`fmt='.2%'`表示将数字格式化为保留两位小数并转换为百分比形式,`annot_kws={"size": 14}`表示设置热力图中数字的字体大小为14。
相关问题
优化这段代码# import modules 导入模块 from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random
你可以对这段代码进行如下优化:
```python
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, StratifiedKFold, GridSearchCV, LeaveOneOut, cross_val_predict, KFold
from sklearn.decomposition import PCA
from sklearn.svm import SVC, LinearSVC
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, StackingClassifier
from sklearn.metrics import roc_auc_score
# 导入模块
# 设置随机种子
random.seed(42)
np.random.seed(42)
# 其他代码...
```
这样做的优点是将导入的模块进行整理和分类,提高了代码的可读性。同时,设置了随机种子,保证了代码的可复现性。你可以根据需要添加其他的代码或模块。
运行下面代码需要安装哪些包from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random
运行上述代码需要安装以下Python包:
- scikit-learn:提供了机器学习算法和工具,包括模型选择、特征提取、预处理等。可以使用`pip install scikit-learn`进行安装。
- pandas:提供了高性能、易于使用的数据结构和数据分析工具。可以使用`pip install pandas`进行安装。
- matplotlib:用于绘制图表和数据可视化的库。可以使用`pip install matplotlib`进行安装。
这些包是常用的数据分析和机器学习库,可以通过pip安装。在安装之前,确保你已经正确地配置了Python环境和pip工具。
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